mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-10-24 07:24:42 +03:00
Rami Mouro
e769ba7a8c
Add a community-supported board build for Qualcomm SoCs with a Hexagon NPU, accelerating TFLite object detection through the QNN TFLite delegate on the Hexagon Tensor Processor (HTP). Supported boards: - IQ9100 (IQ-9075 EVK) - QCS6490 (RB3 Gen 2 Vision Kit / Rubik Pi 3) - detector: new `qualcomm_tfl` plugin loading libQnnTFLiteDelegate.so on the HTP backend, reusing the shared TFLite delegate helpers - docker: `docker/qualcomm` board build (Dockerfile, qualcomm.hcl, qualcomm.mk) producing the arm64 `-qualcomm` image - cdi: per-board Container Device Interface descriptors and an install helper exposing the NPU device nodes and QNN libraries to the container - ci/codeowners: register the qualcomm build target and code owner - docs: installation, hardware, and object detector documentation, using the shared model config dropdown component - i18n: add the Qualcomm detector label and description to the generated en config locale Signed-off-by: Rami Mouro <rmouro@qti.qualcomm.com>
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
"""Qualcomm QNN TFLite delegate detector for Qualcomm NPU acceleration."""
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import logging
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from pydantic import ConfigDict
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from typing_extensions import Literal
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from ..detector_utils import (
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tflite_detect_raw,
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tflite_init,
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tflite_load_delegate_interpreter,
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)
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logger = logging.getLogger(__name__)
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# Use _tfl suffix to default to the bundled tflite model
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DETECTOR_KEY = "qualcomm_tfl"
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class QualcommDetectorConfig(BaseDetectorConfig):
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"""Qualcomm NPU detector using the QNN TFLite delegate to accelerate inference on Qualcomm SoCs with a Hexagon NPU (e.g. IQ9100, QCS6490)."""
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model_config = ConfigDict(
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title="Qualcomm",
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)
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type: Literal[DETECTOR_KEY]
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class QualcommTfl(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: QualcommDetectorConfig):
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# The QNN TFLite delegate library is provided by the host via CDI bind mounts
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delegate_library = "libQnnTFLiteDelegate.so"
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# Use the Hexagon Tensor Processor (HTP / NPU) backend
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device_config = {"backend_type": "htp"}
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interpreter = tflite_load_delegate_interpreter(
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delegate_library, detector_config, device_config
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)
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tflite_init(self, interpreter)
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def detect_raw(self, tensor_input):
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return tflite_detect_raw(self, tensor_input)
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